The vllm-sr organization has released a collection of "Decision 3.0" models, described as open multimodal foundation decision models designed for zero-shot classification. The collection includes multiple model variants of varying sizes to suit different use cases.
- Available model sizes range from a 26B parameter flagship to a 0.6B parameter edge model
- The series includes d3, d3-flash, d3-mini, d3-nano, d3-lite, and d3-edge
- This is the third iteration in the Decision series, following Decision 1.0 and Decision 2.0
- The collection has received 78 upvotes and features 74 unique contributors
- Available model sizes range from a 26B parameter flagship to a 0.6B parameter edge model
- The series includes d3, d3-flash, d3-mini, d3-nano, d3-lite, and d3-edge
- This is the third iteration in the Decision series, following Decision 1.0 and Decision 2.0
- The collection has received 78 upvotes and features 74 unique contributors
Asif Razzaq writes that Microsoft released Microsoft-Decision-1, a model post-trained from Qwen3.5-9B that scores fixed answer options with calibrated probabilities rather than generating text. The model is designed for routing, classification, verification, and agent control, offering a 32,768-token context window, 85 ms p50 latency, and 83.5% average accuracy across 36 internal benchmarks. It is available as a hosted API through Microsoft Foundry and OpenRouter at $0.042 per million input tokens with free output tokens, but it has closed weights and text-only limitations.
- Latency comparisons against competitors are disputed because Microsoft used its own measurement while citing JevBench adjusted figures for others
- The model achieves zero decision flips under paraphrasing, reversal, or option-shuffling perturbations
- Internal Microsoft use cases report 14-100x speed advantages over GPT-6 Sol in feedback sorting and quality control
- Latency comparisons against competitors are disputed because Microsoft used its own measurement while citing JevBench adjusted figures for others
- The model achieves zero decision flips under paraphrasing, reversal, or option-shuffling perturbations
- Internal Microsoft use cases report 14-100x speed advantages over GPT-6 Sol in feedback sorting and quality control
Dan Russell writes about distinguishing between true, correct, and valid claims and offers a six-step verification routine for navigating misinformation. He notes that people often confuse fluency with factuality and that each of the three questions—truth, correctness, and validity—fails in distinct ways requiring different checks. His practical steps include pinning down the claim, noticing emotional reactions, tracing evidence upstream, reading laterally, triangulating with independent sources, and calibrating confidence. He also addresses the specific challenge of verifying what AI tells you, noting that fluent, confident outputs break usual credibility cues, and recommends treating citations as claims to be checked rather than evidence to be trusted.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
tarwin writes about tinyjs, a JavaScript-based desktop app framework that leverages the OS's native webview and a lightweight txiki.js runtime to produce roughly 6 MB binaries across macOS, Windows, and Linux. Unlike Electron or Tauri, it requires no compilation step, bundles no browser engine, and uses no localhost ports, relying instead on Unix sockets or named pipes for secure inter-process communication between the webview and backend.
- The backend provides full system access including files, sockets, FFI, and SQLite.
- macOS is considered stable, while Windows and Linux are currently in beta.
- Developers can scaffold projects with React, Vue, Svelte, or other frameworks using npm, pnpm, or Vite.
- The framework includes a built-in updater that verifies SHA256 hashes and code signatures before swapping in new versions.
- The backend provides full system access including files, sockets, FFI, and SQLite.
- macOS is considered stable, while Windows and Linux are currently in beta.
- Developers can scaffold projects with React, Vue, Svelte, or other frameworks using npm, pnpm, or Vite.
- The framework includes a built-in updater that verifies SHA256 hashes and code signatures before swapping in new versions.
Michal Sutter writes that the vLLM Semantic Router team has released Decision 3.0, a family of five open multimodal decision models ranging from 0.85B to 26.09B parameters. Fine-tuned on Qwen base models, these models accept text, JSON, and images and return calibrated probabilities for typed questions rather than generated text, making them useful for classification and routing tasks.
- All models carry a vision encoder and support multiple image inputs up to 1.6 megapixels each.
- The 9B d3-flash outperforms the previous generation's 27B model, showing significant efficiency gains.
- Benchmarks are self-reported against the Jev Decision Index, with d3 leading at 64.1 on text and 71.6 on vision.
- Median text latency ranges from 21.7 ms for d3-nano to 84 ms for d3 on a single AMD Instinct MI325X GPU.
- All models carry a vision encoder and support multiple image inputs up to 1.6 megapixels each.
- The 9B d3-flash outperforms the previous generation's 27B model, showing significant efficiency gains.
- Benchmarks are self-reported against the Jev Decision Index, with d3 leading at 64.1 on text and 71.6 on vision.
- Median text latency ranges from 21.7 ms for d3-nano to 84 ms for d3 on a single AMD Instinct MI325X GPU.
Simon Batt writes about unidex, an open-source operating system for the 1.54" Waveshare ESP32-S3 ePaper display that turns the tiny board into a pocket productivity device with a Tamagotchi-like virtual pet. It displays the next class with a live countdown, converts voice notes to Markdown for Obsidian, and allows the pet to interact with other users' pets.
- Installable directly from a browser
- Includes a WiFi collection game and four one-button games
- Free to fork and customize for non-commercial purposes
- Installable directly from a browser
- Includes a WiFi collection game and four one-button games
- Free to fork and customize for non-commercial purposes
This documentation introduces how to fine-tune large language models into decision models using Unsloth. These models are designed to make decisions with calibrated probabilities rather than simply generating text. Users can train models like Qwen or Gemma to read input, score provided options, and return a specific choice. The process is supported through the Unsloth Desktop app, free notebooks, or custom Python code, and the resulting model can be integrated directly into the Decision API for practical use.
Ayush Pande writes about running the Cactus Compute Needle 3, a 29MB edge model, on a Raspberry Pi 5 to perform local smart home automation without a dedicated AI HAT. The model uses a unique sliceable ladder architecture that allows it to run on 2 to 20 layers, focusing on machine-level tasks like tool calling and data conversion rather than general chat. He demonstrates how to integrate it with Home Assistant via Python to control a smart plug, achieving impressive decode and prefill speeds.
- The model requires an ARMv7 or newer architecture, meaning it does not run on first-gen Raspberry Pi Zero devices.
- The author achieved a decode rate of 280.2 tokens/second and a prefill rate of 996.4 tokens/second on an 8GB Raspberry Pi 5.
- While designed for specialized tasks, the tool-calling framework is flexible enough to be extended for document analysis or Docker container management.
- The model requires an ARMv7 or newer architecture, meaning it does not run on first-gen Raspberry Pi Zero devices.
- The author achieved a decode rate of 280.2 tokens/second and a prefill rate of 996.4 tokens/second on an 8GB Raspberry Pi 5.
- While designed for specialized tasks, the tool-calling framework is flexible enough to be extended for document analysis or Docker container management.
Benjamin Nweke writes that TypeSafe's Jev model can serve as a lightweight decision layer for agent guardrails, sitting between hard-coded policy checks and human review to catch semantic errors that schema validation misses. Jev returns typed probabilistic answers (allow, review, block) rather than free text, making it faster and cheaper than routing every tool call through another large language model. Nweke emphasizes that Jev should never replace deterministic rules or permission checks; it is best utilized for the fuzzy judgment of whether a proposed action still matches the user's original request.
- Jev is classified as a "System One Model" designed for fast, structured decisions rather than generation
- Typed output guarantees correct shape but not correct content, so a low-confidence decision should fall back to a human
- Nweke recommends tool-specific confidence thresholds rather than one universal threshold
- A positive Jev assessment should never replace the application's own policy checks
- Jev is classified as a "System One Model" designed for fast, structured decisions rather than generation
- Typed output guarantees correct shape but not correct content, so a low-confidence decision should fall back to a human
- Nweke recommends tool-specific confidence thresholds rather than one universal threshold
- A positive Jev assessment should never replace the application's own policy checks
Paul Redmond writes about BashBox, a pure PHP implementation of a bash interpreter created to safely execute shell scripts generated by AI agents without relying on native system calls. The package runs commands against a virtual filesystem with configurable execution limits and restricted network access, supporting standard bash features like pipes, redirections, and control flow alongside 58 built-in commands.
- Uses PHP classes to implement bash commands instead of invoking a shell binary.
- Supports multiple filesystem backends including in-memory, read-write, and overlay.
- Enforces 18 configurable execution limits such as command count and loop iterations.
- Disables network access by default, with optional domain-restricted curl support.
- Requires PHP 8.4 or later with no additional Composer dependencies.
- Uses PHP classes to implement bash commands instead of invoking a shell binary.
- Supports multiple filesystem backends including in-memory, read-write, and overlay.
- Enforces 18 configurable execution limits such as command count and loop iterations.
- Disables network access by default, with optional domain-restricted curl support.
- Requires PHP 8.4 or later with no additional Composer dependencies.
